CN112001531B - Wind power short-term operation capacity credibility assessment method based on effective load capacity - Google Patents

Wind power short-term operation capacity credibility assessment method based on effective load capacity Download PDF

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CN112001531B
CN112001531B CN202010769779.9A CN202010769779A CN112001531B CN 112001531 B CN112001531 B CN 112001531B CN 202010769779 A CN202010769779 A CN 202010769779A CN 112001531 B CN112001531 B CN 112001531B
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陈光宇
王泽宇
张仰飞
郝思鹏
刘海涛
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Nanjing Institute of Technology
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Abstract

The invention discloses a wind power short-term operation capacity credibility assessment method based on effective load capacity, which comprises the following steps: acquiring wind speed data, predicting the wind speed of the next period by using ARMA, and generating a predicted wind power output time sequence; load data are obtained and the combination result of the conventional unit in each period is input; only considering a conventional unit, and calculating the load loss probability LOLP of the system; solving the system load loss probability LOLP again after adding the wind turbine generator; and iteratively solving the wind power short-term operation capacity reliability SOCC through a hybrid search algorithm. The scheme provides a wind power short-term operation capacity credibility definition based on effective load capacity, the proposed evaluation method considers the real-time operation state of the system, reflects the influence of the change of real-time operation conditions such as wind power output, load and the like on the reliability, fully considers the real-time operation state of the system, and the wind power short-term operation credibility capacity of different time periods can reflect the contribution degree of wind power output on the reliability of the system.

Description

Wind power short-term operation capacity credibility assessment method based on effective load capacity
Technical Field
The invention relates to a wind power short-term operation capacity credibility assessment method based on effective load capacity, and belongs to a wind power capacity credibility assessment technology.
Background
The intermittent energy source represented by wind power is accessed in a large scale in the global scope, so that the operation mode of the power system is greatly changed. The wind power with uncertainty and intermittence is introduced at the power generation side, so that great challenges are brought to the power system in maintaining the balance between the generated energy and the load demand. Because the output of the wind turbine has the characteristic of intermittence, the load carrying capacity of the wind turbine with the same capacity is different from that of the conventional wind turbine. Therefore, in the power system adequacy measurement and analysis, the power department treats the wind turbine generator set differently from the conventional wind turbine generator set.
The research on the reliability of the existing wind power capacity is mainly focused on a planning state and is not applicable to the field of power dispatching. The reliability of the planned wind power capacity cannot be directly applied to the dispatching operation work, otherwise, load distribution is improper, standby is excessively arranged, and the method is contrary to the requirement of economic operation and causes a serious wind abandoning phenomenon. The current wind power short-term operation capacity reliability assessment method omits the process of reliability index iterative solution, so that the assessment method lacks accuracy.
Disclosure of Invention
The invention aims to: in order to overcome the defects in the prior art, the invention provides a wind power short-term operation capacity reliability assessment method based on effective load capacity, and provides definition of wind power short-term operation capacity reliability SOCC based on the effective load capacity; meanwhile, based on the existing wind power prediction method, ARMA is used for predicting wind speed and generating a wind power output time sequence; respectively solving the system load loss probability LOLP before and after the wind turbine generator is added by using an ACD method; and iteratively solving the wind power short-term operation capacity reliability SOCC through a hybrid search algorithm.
The technical scheme is as follows: in order to achieve the above purpose, the invention adopts the following technical scheme:
a wind power short-term operation capacity credibility assessment method based on effective load capacity comprises the following steps:
s1, acquiring wind speed data, and predicting wind speed by using ARMA modelTime series V t ；
S2, wind speed time sequence V t Conversion to wind power output time sequence P wt ；
S3, obtaining load data and a thermal power generating unit combination result of each period;
s4, only considering an initial system formed by K thermal power generating units, and calculating the load loss probability LOLP of the initial system by using an ACD method t,K ；
S5, adding M wind turbines into the initial system, and calculating new system load loss probability LOLP by using an ACD method t,M+K ；
S6, iteratively solving wind power short-term operation credible capacity C through hybrid search algorithm W (t)；
S7, short-term operation credible capacity C based on wind power W (t) solving the reliability SOCC of the short-term running capacity of wind power t 。
Preferably, in the step S1, wind speed data is acquired, and the ARMA model is used to predict the wind speed time series V t The method comprises the following steps:
s11, preprocessing wind speed data to obtain a time sequence { x } t First calculate { x } t Auto-correlation function ρ k Observing whether it decays rapidly to around 0; if { x t If the first order difference is not satisfied, judging again; if the stability requirement cannot be met after the difference is carried out twice, { x } t No wind speed can be predicted using ARMA;
s12, model identification and order determination are carried out according to { x } t Autocorrelation coefficient ρ of } k And partial autocorrelation coefficientSelecting a proper model, and selecting a proper model order by using an AIC criterion;
the autocorrelation function (ACF) is defined as:
wherein: gamma ray k Is covariance, gamma 0 And sigma (sigma) x 2 Variance, mu x K is hysteresis order (positive integer) and n is the number of observed values (the number of observed values in the sequence);
the Partial Autocorrelation Coefficient (PACF) is defined as:
the AIC criteria are defined as:
wherein: n is the number of observed values; p and q are the orders of the model; sigma (sigma) a Variance of residual errors when fitting the model; taking the order and the parameter corresponding to the minimum AIC value as the optimal order and the parameter of the ARMA model;
s13, carrying out model parameter estimation by adopting maximum likelihood estimation, and calling an Estimate function in MATLAB to realize a parameter estimation function;
s14, checking whether the model has first-order correlation or not by using DW statistics (Durbin-Watson statistical), and if the output DW function value is close to 2, considering that the first-order correlation does not exist;
s15, forecasting wind speed time sequence V t The autoregressive moving average model ARMA is expressed mathematically as follows:
x t ＝a 1 x t-1 +a 2 x t-2 +···+a p x t-p +ε t -b 1 ε t-1 -b 2 ε t-2 -···-b q ε t-q
t＝1,2,···,N
wherein: epsilon t Is a white noise sequence; a, a 1 ,a 2 ,···,a p Is an autoregressive coefficient; b 1 ,b 2 ,···,b q Is a running average coefficient; p and q are the orders of the model; { x t And the current time series value.
Preferably, in the step S2, the wind speed time series V t With wind power output time series P wt The relationship between them is as follows:
wherein: v (V) ci 、V co 、V r The wind speed is cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine generator; v (V) t The wind speed is the current moment; A. b, C about V ci And V r Is a function of (2); p (P) r The rated output of the wind turbine generator is provided; A. b, C is expressed as follows:
preferably, in the step S3, the load data is timelyProbability pulse representation of sequential load; let the study period be T hours, and the load level at T hours be L t T=1, 2, …, T, probability of occurrence of load level at T hours is(the purpose of this step is to simplify the resolution of the reliability index in ACD), then the load data over the study period is denoted as l= { L 1 ,L 2 ,···,L t ,···,L T }。
Preferably, in the step S4, an ACD method is used to calculate an initial system load loss probability LOLP for the initial system t,K The method comprises the following steps:
s41, the effective capacity distribution of the thermal power generating unit i is expressed as:
wherein: i=1, 2, …, K, C i Is the installed capacity of the thermal power generating unit i,is the effective capacity distribution of the thermal power unit i, P FOR,i The probability of random outage of the thermal power unit i is obtained;
s42, installed capacity C of thermal power generating unit i i For discrete random variables, a discrete random variable C is defined i The v-order moment of (2) is:
p i ＝1-P FOR,i
wherein: p is p i Is the normal operation probability of the thermal power generating unit i,is v-order moment of installed capacity of thermal power unit i, alpha v V-order moment which is the effective capacity of the thermal power generating unit, wherein v is a positive integer;
s43, converting each order moment into each order center moment:
wherein: m is M v Is the v-order central moment of the effective capacity of the thermal power generating unit,taking n as v and arranging and combining;
s44, obtaining the accumulation amount of each order through the center distance of each order, wherein the relation between the accumulation amount of the first 8 orders and the center moment of each order is as follows:
wherein: k (K) v The v-order accumulated quantity is the effective capacity of the thermal power generating unit;
s45, the accumulated quantity of each step of equivalent effective capacity of the K thermal power generating units is expressed as follows:
wherein: KS (KS) v The v-order cumulant for equivalent effective capacity of K thermal power generating units, K i,v The v-order cumulative quantity of the thermal power unit i;
s46, the distribution of equivalent effective capacity is expressed by accumulation through edge worth series expansion, and F is used as a distribution function of the effective capacity after the loading of the K thermal power generating units K (x) The representation is:
wherein: f (F) K (x) The probability that the power generation capacity provided after loading of K thermal power generating units is smaller than x is that N (x) is a standard normal density function, N (γ) (x) Gamma derivative of N (x), g v Normalized cumulants for v-th order (the term is introduced for simplicity in the form of a series), σ is the standard deviation;
s47, load level L for t hour t Using F K (L t ) Indicating that the power generation capacity at t hour is less than L t To obtain the probability of load loss LOLP at t hour t,K And F is equal to K (L t ) Is related to LOLP t,K ＝F K (L t )。
Preferably, in the step S5, M wind turbines are added into the initial system, and the ACD method is used to calculate the load loss probability LOLP of the new system t,M+K The method comprises the following steps:
s51, the effective capacity distribution of the wind turbine j is expressed as:
wherein: j=1, 2, …, M, P W,j (t) is the output of the wind turbine j at the moment t,for the effective capacity distribution of the wind turbine j, P FOR,Wj The forced outage probability of the wind turbine j is set;
s52, wind motorOutput P of group j W,j For discrete random variables, a discrete random variable P is defined W,j The v-order moment of (2) is:
p Wj ＝1-P FOR,Wj
wherein: p is p Wj P is the normal operation probability of the wind turbine j W,j Output of wind turbine j, beta v V-order moment which is the effective capacity of the wind turbine generator, wherein v is a positive integer;
s53, converting each order moment into each order center moment, wherein the formula is as follows:
wherein: m is M Wv Is the v-order central moment of the effective capacity of the wind turbine,taking n as v and arranging and combining;
s54, obtaining the accumulation amount of each order through the center distance of each order, wherein the relation between the accumulation amount of the first 8 orders and the center moment of each order is as follows:
wherein: k (K) Wv V-order cumulant for the effective capacity of the wind turbine;
s55, the accumulated quantity of each step of equivalent effective capacity of M wind turbines and K thermal power turbines is expressed as:
wherein: KW (KW) v For the v-order cumulant of equivalent effective capacity of M wind turbines and K thermal power turbines, KS v The v-order cumulant for equivalent effective capacity of K thermal power generating units, K Wj,v The v-order cumulant of the wind turbine j;
s56, the distribution of equivalent effective capacity is represented by accumulation through edge worth series expansion, and the distribution function of the effective capacity after the loading of M wind turbines and K thermal power turbines is represented by F M+K (x) The representation is:
wherein: f (F) M+K (x) Probability that the power generation capacity provided after loading of M wind turbines and K thermal power turbines is smaller than x; n (x) is a standard normal density function; n (N) (γ) (x) Gamma derivative for N (x); g Wv Normalized cumulants for v-th order (the term is introduced for simplicity in the form of a series), σ is the standard deviation;
s57, load level L for t hour t Using F M+K (L t ) Indicating that the power generation capacity at t hour is less than L t To obtain the probability of load loss LOLP at t hour t,M+K And F is equal to M+K (L t ) Is related to LOLP t,M+K ＝F M+K (L t )。
Preferably, in the step S6, a short-term operation of wind power is solvedTrusted capacity C W (t) comprises the steps of:
s61, calculating maximum load loss probability of a system formed by M wind turbines and K thermal power plantsAnd F is equal to M+K (x) The following relationship exists:
wherein:for the maximum load loss probability of a system formed by M wind turbines and K thermal power plants, L t At load level at t hours, C W The total capacity of the wind turbine generator is set;
s62, aiming at a system formed by M wind turbines and K thermal power plants, calculating the intermediate value of the maximum load loss probability of the systemAnd F is equal to M+K (x) The following relationship exists:
wherein:is the intermediate value of the maximum load loss probability of a system formed by M wind turbines and K thermal power plants, L t At load level at t hours, C W The total capacity of the wind turbine generator is set;
s63, assigning the load loss probability data at the t hour:
R 0 (t)＝LOLP t,K
R W (t)＝LOLP t,M+K
s64 by bringing in different DeltaL t Iterative solution R q (t); let iteration number q=1, compare R 0 (t) and R mid Size of the value of (t): if the absolute value of the difference is greater than 5 ε, i.e. |R 0 (t)-R mid (t) | is not less than 5 epsilon, and the step S65 is carried out; otherwise, step S66 is entered; wherein epsilon is the precision requirement of iterative solution, deltaL is the total capacity of the initial system added loader is C W The load quantity which can be borne by the wind turbine generator;
s65, solving target value R by chord cut method q (t) if |R q (t)-R 0 (t)|＞|R q-1 (t)-R 0 (t) |, indicating that the oscillation phenomenon is generated during the q-th iteration, and proceeding to step S66; otherwise, q=q+1, repeating step S65, and continuing to iteratively solve the target value R using the chord cut method q (t)；
S66, solving target value R by using dichotomy q (t), wherein R is q (t) obtaining a reliability index for the (t) th time and the (q) th time iteration, and entering step S67;
s67, if|R q (t)-R 0 (t) |ε or more, then q=q+1, repeat step S66, continue to iteratively solve the target value R using the dichotomy q (t); otherwise, step S68 is entered;
s68, taking a load side as an access point, and calculating the ratio of the load quantity capable of being born more to the installed capacity of wind power under the condition that the reliability level of the system is kept unchanged before and after the wind power generation set is accessed:
R(C g ,L)＝R(C g +C W ,L+ΔL)
C W (t)＝ΔL t
wherein: c (C) W (t) is the wind power short-term operation credible capacity of the t hour; ΔL t Adding installed total capacity to initial System of C W The load quantity of multiple loads which can be born at t hours after the wind turbine generator, C g For the installed total capacity of the initial system (installed total capacity of the thermal power unit), R (C g L) is the reliability level of the initial system in the face of load L, R (C) g +C W L+Δl) is the level of reliability when the new system after adding wind turbines to the initial system is subjected to a load l+Δl.
Preferably, in the step S7, the wind power short-term operation capacity reliability SOCC t Solving according to the following formula:
wherein: SOCC (solid State control) t For the reliability of short-term running capacity of the wind power at the t-th hour, C W Is the total capacity of the wind turbine generator, delta L t Adding installed total capacity to initial System of C W The load quantity which can be borne more at the t hour after the wind turbine generator.
The beneficial effects are that: the wind power short-term operation capacity reliability assessment method based on the payload capacity provided by the invention can be used for carrying out reliability assessment under the premise of considering the real-time operation state of the system, and a hybrid search algorithm is used for iterative solution of the wind power short-term operation reliability capacity, so that the influence of search oscillation due to a chord cut method on the iterative times can be well avoided, and the solution speed and precision can be accelerated; according to the method, reliability analysis is carried out according to the real-time running state of the system, so that the influence of the wind power output time sequence characteristic and uncertainty thereof on the reliability of the short-term running capacity of wind power can be well reflected; the wind power short-term operation credible capacity of different time periods can reflect the contribution degree of wind power output to the reliability of the system.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a diagram of a geometrical interpretation of the trusted capacity based on payload capacity in the present invention;
FIG. 3 is a flow chart of a hybrid search algorithm of the present invention;
FIG. 4 is a graph of reliability index comparisons before and after wind power access;
FIG. 5 is a graph of SOCC and wind power short-term operation reliability versus capacity for each period.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
The wind power short-term operation capacity reliability assessment method based on the payload capacity comprises the following steps of:
s1, acquiring wind speed data, and predicting a wind speed time sequence V by using an ARMA model t 。
S11, when the ARMA model predicts data, the time sequence of sample data needs to meet the stability requirement; the stationarity is that a fitting curve obtained through a sample time sequence is required to be continued along the inertia of the existing form in a future period of time, namely the mean value and the variance of the fitting curve are required not to be changed significantly; therefore, whether the original wind speed sequence is a stable random sequence or not is detected before wind speed prediction modeling is carried out; preprocessing wind speed data to obtain a time sequence { x } t First calculate { x } t Auto-correlation function ρ k Observing whether it decays rapidly to around 0; if { x t If the first order difference is not satisfied, judging again; if the stability requirement cannot be met after the difference is carried out twice, { x } t No wind speed can be predicted using ARMA;
s12, model identification and order determination are carried out according to { x } t Autocorrelation coefficient ρ of } k And partial autocorrelation coefficientSelecting a proper model; in the actual data processing and analyzing process, the values of the orders p and q of the ARMA model are not unique, and if the values are estimated by human beings, errors are necessarily generated, so that the AIC criterion is used for selecting the proper model order:
the autocorrelation function (ACF) is defined as:
wherein: gamma ray k Is covariance, gamma 0 And sigma (sigma) x 2 Variance, mu x K is hysteresis order, n is the number of observed values;
the Partial Autocorrelation Coefficient (PACF) is defined as:
selecting a proper model order by using a red pool information criterion (AIC criterion), wherein the criterion utilizes the principle that the likelihood function estimated value is maximum to determine the model order; the AIC criteria are defined as:
wherein: n is the number of observed values; p and q are the orders of the model; sigma (sigma) a Variance of residual errors when fitting the model; taking the order and the parameter corresponding to the minimum AIC value as the optimal order and the parameter of the ARMA model;
s13, after determining the model structure and the order and establishing an ARMA model, estimating model parameters; the common parameter estimation method comprises moment estimation, maximum likelihood estimation and least square estimation, wherein the moment estimation only uses p+q sample information, so that the information redundancy is excessive, and the estimation precision is poor; the scheme adopts maximum likelihood estimation to Estimate model parameters, and an Estimate function is called in MATLAB to realize a parameter estimation function;
s14, a common ARMA model checking method comprises stable reversibility checking, overfitting checking and residual analysis checking, wherein some documents adopt residual analysis checking, the method checks whether model residual has certain randomness, and if the model residual does not have randomness, the selected model is required to be modified; the DW Statistic (Durbin-Watson static) is used for checking whether the model has first-order correlation, and if the DW function value is close to 2, the model is considered to have no first-order correlation;
s15, forecasting wind speed time sequence V t The autoregressive moving average model ARMA is expressed mathematically as follows:
x t ＝a 1 x t-1 +a 2 x t-2 +···+a p x t-p +ε t -b 1 ε t-1 -b 2 ε t-2 -···-b q ε t-q
t＝1,2,···,N
wherein: epsilon t Is a white noise sequence; a, a 1 ,a 2 ,···,a p Is an autoregressive coefficient; b 1 ,b 2 ,···,b q Is a running average coefficient; p and q are the orders of the model; { x t And the current time series value.
S2, wind speed time sequence V t Conversion to wind power output time sequence P wt 。
Predicting the wind speed V at the time t according to an ARMA model t Then, the wind power output power at the time t can be calculated; the uncertainty of wind speed is included in the ARMA model using the wind power prediction method described above, so that for a certain determined time t, its wind speed V t Is relatively determined, wind speed time series V t With wind power output time series P wt The relationship between them is as follows:
wherein: v (V) ci 、V co 、V r The wind speed is cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine generator; v (V) t The wind speed is the current moment; A. b, C about V ci And V r Is a function of (2); p (P) r The rated output of the wind turbine generator is provided; A. b, C is expressed as follows:
and S3, acquiring load data and a thermal power generating unit combination result of each period.
Load data is represented by probability pulses of time-series load; let the study period be T hours, and the load level at T hours be L t T=1, 2, …, T, probability of occurrence of load level at T hours is(the purpose of this step is to simplify the resolution of the reliability index in ACD), then the load data over the study period is denoted as l= { L 1 ,L 2 ,···,L t ,···,L T }。
S4, only considering an initial system formed by K thermal power generating units, and calculating the load loss probability LOLP of the initial system by using an ACD method t,K 。
S41, representing a common two-state model of the thermal power unit, wherein the effective capacity distribution of the thermal power unit i is represented asThe thermal power generating units are ordered according to the power generation cost and loaded in sequence, so that the effective capacity of the front K thermal power generating units is +.>And->The relationship is as follows:
wherein: i=1, 2, …, K, C i Is the installed capacity of the thermal power generating unit i,is the effective capacity distribution of the thermal power unit i, P FOR,i The probability of random outage of the thermal power unit i is obtained;
s42, as the scale of the power system is enlarged, convolution and deconvolution calculation amount is too large each time, so the ACD method describes the load level of each period of the system and the random shutdown condition of a unit according to the accumulation amount of each step, the method simplifies the convolution calculation into addition calculation of a plurality of accumulation amounts, greatly simplifies the calculation difficulty, and after each step of accumulation amount of an effective capacity distribution curve is obtained, the function value of each point on the curve can be obtained through the edge number expansion, thereby calculating the required reliability index; installed capacity C of thermal power generating unit i i For discrete random variables, a discrete random variable C is defined i The v-order moment of (2) is:
p i ＝1-P FOR,i
wherein: p is p i Is the normal state of the thermal power generating unit iThe probability of operation is determined by the probability of operation,is v-order moment of installed capacity of thermal power unit i, alpha v V-order moment which is the effective capacity of the thermal power generating unit, wherein v is a positive integer;
s43, cumulative amount K v A numerical feature, also a random variable, which can be found from moments of orders not higher than the corresponding order; to simplify the operation, each order moment is converted into each order center moment:
wherein: m is M v Is the v-order central moment of the effective capacity of the thermal power generating unit,taking n as v and arranging and combining;
s44, obtaining the accumulation amount of each order through the center distance of each order, wherein the relation between the accumulation amount of the first 8 orders and the center moment of each order is as follows:
wherein: k (K) v The v-order accumulated quantity is the effective capacity of the thermal power generating unit;
s45, according to the additivity of the accumulation amount, when the effective capacity distributions of the thermal power generating units are independent of each other, the equivalent effective capacity distribution function can be realized through the addition operation of the accumulation amount to replace convolution operation, so that the calculation difficulty is simplified; therefore, the accumulated quantities of the steps of the equivalent effective capacity of the K thermal power generating units are expressed as follows:
wherein: KS (KS) v The v-order cumulant for equivalent effective capacity of K thermal power generating units, K i,v The v-order cumulative quantity of the thermal power unit i;
s46, the distribution of equivalent effective capacity is expressed by accumulation through edge worth series expansion, and F is used as a distribution function of the effective capacity after the loading of the K thermal power generating units K (x) The representation is:
wherein: f (F) K (x) The probability that the power generation capacity provided after loading of K thermal power generating units is smaller than x is that N (x) is a standard normal density function, N (γ) (x) Gamma derivative of N (x), g v Normalized cumulants for v-th order (the term is introduced for simplicity in the form of a series), σ is the standard deviation;
s47, load level L for t hour t Using F K (L t ) Indicating that the power generation capacity at t hour is less than L t To obtain the probability of load loss LOLP at t hour t,K And F is equal to K (L t ) Is related to LOLP t,K ＝F K (L t )。
S5, adding M wind turbines into the initial system, and calculating new system load loss probability LOLP by using an ACD method t,M+K 。
S51, after a wind farm is added into an initial system, preferentially loading wind turbines, and then sequentially loading conventional generators; describing a wind turbine generator by using a two-state model, wherein the effective capacity distribution of the wind turbine generator j is expressed as follows:
wherein: j=1, 2, …, M, P W,j (t) is the output of the wind turbine j at the moment t,for the effective capacity distribution of the wind turbine j, P FOR,Wj The forced outage probability of the wind turbine j is set;
s52, output P of wind turbine j W,j For discrete random variables, a discrete random variable P is defined W,j The v-order moment of (2) is:
p Wj ＝1-P FOR,Wj
wherein: p is p Wj P is the normal operation probability of the wind turbine j W,j Output of wind turbine j, beta v V-order moment which is the effective capacity of the wind turbine generator, wherein v is a positive integer;
s53, converting each order moment into each order center moment, wherein the formula is as follows:
wherein: m is M Wv Is the v-order central moment of the effective capacity of the wind turbine,taking n as v and arranging and combining;
s54, obtaining the accumulation amount of each order through the center distance of each order, wherein the relation between the accumulation amount of the first 8 orders and the center moment of each order is as follows:
wherein: k (K) Wv V-order cumulant for the effective capacity of the wind turbine;
s55, the accumulated quantity of each step of equivalent effective capacity of M wind turbines and K thermal power turbines is expressed as:
wherein: KW (KW) v For the v-order cumulant of equivalent effective capacity of M wind turbines and K thermal power turbines, KS v The v-order cumulant for equivalent effective capacity of K thermal power generating units, K Wj,v The v-order cumulant of the wind turbine j;
s56, the distribution of equivalent effective capacity is represented by accumulation through edge worth series expansion, and the distribution function of the effective capacity after the loading of M wind turbines and K thermal power turbines is represented by F M+K (x) The representation is:
wherein: f (F) M+K (x) Probability that the power generation capacity provided after loading of M wind turbines and K thermal power turbines is smaller than x; n (x) is a standard normal density function; n (N) (γ) (x) Gamma derivative for N (x); g Wv Normalized cumulants for v-th order (the term is introduced for simplicity in the form of a series), σ is the standard deviation;
s57, load level L for t hour t Using F M+K (L t ) Indicating that the power generation capacity at t hour is less than L t To obtain the probability of load loss LOLP at t hour t,M+K And F is equal to M+K (L t ) Is related to LOLP t,M+K ＝F M+K (L t )。
S6, iteratively solving wind power short-term operation credible capacity C through hybrid search algorithm W (t)。
When the wind turbine generator is not considered, an equivalent effective capacity distribution curve F of the K thermal power generating units is obtained through an ACD method K (x) The method comprises the steps of carrying out a first treatment on the surface of the After M wind turbines are added, an equivalent effective capacity distribution curve F of a new system is obtained M+K (x) The method comprises the steps of carrying out a first treatment on the surface of the Load L facing t hour t At the time, the load shedding probability is LOLP t,K And LOLP t,M+K Because the effective capacity distribution function F (x) of the unit is complex when the reliability index of the system is solved by the ACD method, when the target value LOLP is known t,K When the method can not solve the required x by a method of negating the function, iterative calculation can be carried out by modifying x; solving for LOLP t,M+K ′＝LOLP t,K L at the time t +ΔL t Δl at this time t The wind power short-term operation credible capacity is obtained.
S61, calculating maximum load loss probability of a system formed by M wind turbines and K thermal power turbines by using the method of the step S5By definition, let t hours consider only the system reliability index of the conventional unit as LOLP t,K The adding force is P Wt The reliability index of the system behind the wind turbine generator is LOLP t,M+K The method comprises the steps of carrying out a first treatment on the surface of the If the newly-added power supply is an ideal unit with hundred percent reliability, thenIts reliability should be LOLP St max However, due to the random outage rate of the wind turbine and the existence of other fault factors, the trusted capacity of the newly-increased wind turbine is between 0 and the wind turbine installed capacity C W Between them; />And F is equal to M+K (x) The following relationship exists:
wherein:for the maximum load loss probability of a system formed by M wind turbines and K thermal power plants, L t At load level at t hours, C W The total capacity of the wind turbine generator is set;
s62, calculating the intermediate value of the maximum load loss probability of a system formed by M wind turbines and K thermal power turbines by using the method of the step S5And F is equal to M+K (x) The following relationship exists:
wherein:is the intermediate value of the maximum load loss probability of a system formed by M wind turbines and K thermal power plants, L t At load level at t hours, C W The total capacity of the wind turbine generator is set;
s63, when a chord cut method is used, oscillation occurs when the chord cut method is close to the optimal value, so that the optimal solution meeting the precision requirement cannot be converged, and the iteration times are increased; when the dichotomy is used, the searching speed is slower than that of the chord cut method, but the optimal solution is always found as the searching range is continuously reduced; aiming at the advantages and disadvantages of a comprehensive search planning type wind power capacity credibility algorithm, a hybrid search algorithm is provided; assigning the load loss probability data at the t hour:
R 0 (t)＝LOLP t,K
R W (t)＝LOLP t,M+K
s64 by bringing in different DeltaL t Iterative solution R q (t); firstly, judging the interval position of a target value, and accurately searching by using a dichotomy when the interval position is close to the target value; if the distance from the target value is far, the chord cut method is adopted to accelerate the search until the distance is close to the target value, and the method is switched to the dichotomy accurate search; the method comprises the following steps: let iteration number q=1, compare R 0 (t) and R mid Size of the value of (t): if the absolute value of the difference is greater than 5 ε, i.e. |R 0 (t)-R mid (t) | is not less than 5 epsilon, and the step S65 is carried out; otherwise, step S66 is entered; wherein epsilon is the precision requirement of iterative solution, deltaL is the total capacity of the initial system added loader is C W The load quantity which can be borne by the wind turbine generator;
s65, solving target value R by chord cut method q (t) if |R q (t)-R 0 (t)|＞|R q-1 (t)-R 0 (t) |, then indicates that oscillation occurs at the q-th iteration, R q (t) swinging around the target value, so that a convergence iterative operation using a dichotomy is required, proceeding to step S66; otherwise, no oscillation is generated, q=q+1, step S65 is repeated, and the objective value R is iteratively solved by continuing the chord cut method q (t)；
S66, solving target value R by using dichotomy q (t), wherein R is q (t)Step S67 is carried out for the reliability index solved by the (th) iteration of the (th) hour;
s67, if|R q (t)-R 0 (t) |is not less than epsilon, if the calculation result is that the accuracy requirement is met, q=q+1, repeating the step S66, and continuously using a dichotomy to iteratively solve the target value R q (t); otherwise, step S68 is entered;
s68, taking a load side as an access point, and calculating the ratio of the load quantity capable of being born more to the installed capacity of wind power under the condition that the reliability level of the system is kept unchanged before and after the wind power generation set is accessed:
R(C g ,L)＝R(C g +C W ,L+ΔL)
C W (t)＝ΔL t
wherein: c (C) W (t) is the wind power short-term operation credible capacity of the t hour; ΔL t Adding installed total capacity to initial System of C W The load quantity of multiple loads which can be born at t hours after the wind turbine generator, C g For the installed total capacity of the initial system (installed total capacity of the thermal power unit), R (C g L) is the reliability level of the initial system in the face of load L, R (C) g +C W L+Δl) is the level of reliability when the new system after adding wind turbines to the initial system is subjected to a load l+Δl.
S7, short-term operation credible capacity C based on wind power W (t) solving the reliability SOCC of the short-term running capacity of wind power t 。
The reliability level can be changed due to the change of real-time operation conditions such as wind power output, load and the like, so that the reliability SOCC of the short-term operation capacity of wind power is realized t Fully considers the real-time running state of the system; the short-term running capacity reliability expression of wind power is as follows:
wherein: SOCC (solid State control) t For the reliability of short-term running capacity of the wind power at the t-th hour, C W Is the total capacity of the wind turbine generator, delta L t Adding installed total capacity to initial System of C W The load quantity which can be borne more at the t hour after the wind turbine generator.
According to the method, the reliability of the short-term running capacity of the final wind power can be obtained.
One specific embodiment of the wind power short-term operation capacity reliability assessment method based on the effective load capacity is as follows:
taking an IEEE-RTS79 reliability test system as an example to carry out example simulation, removing 3105MW of total assembly machine capacity of the system after the hydroelectric generating set in the system, and equivalent the nuclear power generating set to a conventional thermal power generating set. The wind speed prediction data of a certain place of Jiangsu is used, wind power output is modeled, 108 wind power units are provided, the capacity of a total assembly machine is 162MW, the wind speeds of the units cut in, rated and cut out are 3.33 m/s, 13.55 m/s and 22.22m/s respectively, and the random outage rate is 0.04. The study period took 24 hours, with 400MW of spare capacity configured for each period.
TABLE 1 wind power output prediction data
The data in the table is measured wind speed data of a Jiangsu certain wind farm, and the measured wind speed data is sampled every 10 minutes, and the total number of the sampling points is 430. Selecting the first 286 sampling point data as original wind speed data, and predicting wind speed data of 144 future sampling points; and obtaining wind power output prediction data in the table according to the relation between the wind speed and the output of the wind turbine generator, wherein the total time is 24 hours.
TABLE 2 load data
The data in the table are data from day 23 of the week of the IEEE-RTS79 reliability test system. And respectively calculating the reliability indexes LOLP of the systems before and after wind power access by using the data, wherein the result is shown in figure 4. By comparison, the reliability index LOLP of the system changes along with the fluctuation of the load before wind power is connected, and the reliability of each period is different; the reliability of the system is improved after wind power is connected, but the LOLP value fluctuation in partial time period is larger due to the randomness of wind power output. Although the wind power output is a predicted value and a certain degree of error exists, the addition of a certain amount of wind power output is still proved to be helpful for improving the reliability of the system.
TABLE 3 SOCC and wind Power short-term operation trusted Capacity for each period
The data in the table are obtained by iteratively solving the wind power short-term operation credibility by using a hybrid search algorithm according to the wind power short-term operation credibility definition based on the effective load capacity. The wind power short-term credible capacity and SOCC calculation results of each period are shown in FIG. 5. And the wind power credible capacity of each period represents the load quantity which can be born by the newly increased wind power output when the reliability of the system is equal before and after wind power is connected. By comparison, the short-term reliability capacity of the wind power is lower than the predicted output of the wind power under the double influences of the randomness of the wind power in each period and the unchanged reliability level; by comparing the figures 4 and 5, when the reliability level difference between the system before and after the wind power is connected at 14 time and 15 time is larger, the short-term operation reliability capacity and SOCC of the wind power are larger; and when the reliability level difference between 1 time and 24 time is smaller, the short-term operation reliability capacity and SOCC of wind power are smaller, and the wind power utilization rate is more than 95%.
Table 4 comparison of trusted capacity search algorithms
The result of the comparison of the above SOCC solution calculation example with the chord cut method and the dichotomy using the trusted capacity search algorithm proposed by the present invention is shown in Table 4. By comparison, when the precision is lower, the difference of the iteration times required by the three methods is not large; and when the precision requirement is higher, the method can still search the optimal solution through the minimum iteration times. Therefore, the searching algorithm provided by the invention can be embodied, and the advantages are that the distance between the optimal solution and the iteration value is judged, and then the chord cut method or the dichotomy method is used for searching according to the distance judgment, so that the oscillation problem generated by the chord cut method when the precision requirement is high is avoided to a great extent.
In summary, the invention provides a wind power short-term operation capacity credibility definition which is suitable for considering the real-time operation state of a system; the reliability evaluation based on the effective capacity distribution accumulation method is provided, and the reliability evaluation can be performed on the premise of considering the real-time running state of the system; the mixed search algorithm is used for iterative solution of wind power short-term operation credible capacity, can well avoid influence of search oscillation due to a chord cut method on iteration times, and accelerates solving speed and precision; according to the method, reliability analysis is carried out according to the real-time running state of the system, so that the influence of the wind power output time sequence characteristic and uncertainty thereof on the reliability of the short-term running capacity of wind power can be well reflected; the wind power short-term operation credible capacity of different time periods can reflect the contribution degree of wind power output to the reliability of the system.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.

Claims (4)

1. A wind power short-term operation capacity credibility assessment method based on effective load capacity is characterized by comprising the following steps of: the method comprises the following steps:
s1, acquiring wind speed data, and predicting a wind speed time sequence V by using an ARMA model t ；
S2, wind speed time sequence V t Conversion to wind power output time sequence P wt ；
S3, obtaining load data and a thermal power generating unit combination result of each period;
s4, only considering an initial system formed by K thermal power generating units, and calculating the load loss probability LOLP of the initial system by using an ACD method t,K The method comprises the steps of carrying out a first treatment on the surface of the The method comprises the following steps:
s41, the effective capacity distribution of the thermal power generating unit i is expressed as:
wherein: i=1, 2, …, K, C i Is the installed capacity of the thermal power generating unit i,is the effective capacity distribution of the thermal power unit i, P FOR,i The probability of random outage of the thermal power unit i is obtained;
s42, installed capacity C of thermal power generating unit i i For discrete random variables, a discrete random variable C is defined i The v-order moment of (2) is:
p i ＝1-P FOR,i
wherein: p is p i Is the normal operation probability of the thermal power generating unit i,is v-order moment of installed capacity of thermal power unit i, alpha v V-order moment which is the effective capacity of the thermal power generating unit, wherein v is a positive integer;
s43, converting each order moment into each order center moment:
wherein: m is M v Is the v-order central moment of the effective capacity of the thermal power generating unit,taking n as v and arranging and combining;
s44, obtaining the accumulation amount of each order through the center distance of each order, wherein the relation between the accumulation amount of the first 8 orders and the center moment of each order is as follows:
wherein: k (K) v The v-order accumulated quantity is the effective capacity of the thermal power generating unit;
s45, the accumulated quantity of each step of equivalent effective capacity of the K thermal power generating units is expressed as follows:
wherein: KS (KS) v The v-order cumulant for equivalent effective capacity of K thermal power generating units, K i,v The v-order cumulative quantity of the thermal power unit i;
s46, the distribution of equivalent effective capacity is expressed by accumulation through edge worth series expansion, and F is used as a distribution function of the effective capacity after the loading of the K thermal power generating units K (x) The representation is:
wherein: f (F) K (x) The probability that the power generation capacity provided after loading of K thermal power generating units is smaller than x is that N (x) is a standard normal density function, N (γ) (x) Gamma derivative of N (x), g v The normalized cumulative quantity is of v order, and sigma is the standard deviation;
s47, load level L for t hour t Using F K (L t ) Indicating that the power generation capacity at t hour is less than L t To obtain the probability of load loss LOLP at t hour t,K And F is equal to K (L t ) Is related to LOLP t,K ＝F K (L t )；
S5, adding M wind turbines into the initial system, and calculating new system load loss probability LOLP by using an ACD method t,M+K ；
S6, iteratively solving wind power short-term operation credible capacity C through hybrid search algorithm W (t); the method comprises the following steps:
s61, calculating maximum load loss probability of a system formed by M wind turbines and K thermal power plantsAnd F is equal to M+K (x) The following relationship exists:
wherein:for the maximum load loss probability of a system formed by M wind turbines and K thermal power plants, L t At load level at t hours, C W The total capacity of the wind turbine generator is set;
s62, aiming at a system formed by M wind turbines and K thermal power plants, calculating the intermediate value of the maximum load loss probability of the systemAnd F is equal to M+K (x) The following relationship exists:
wherein:is the intermediate value of the maximum load loss probability of a system formed by M wind turbines and K thermal power plants, L t At load level at t hours, C W The total capacity of the wind turbine generator is set;
s63, assigning the load loss probability data at the t hour:
R 0 (t)＝LOLP t,K
R W (t)＝LOLP t,M+K
s64 by bringing in different DeltaL t Iterative solution R q (t); let iteration number q=1, compare R 0 (t) and R mid Size of the value of (t): if the absolute value of the difference is greater than 5 ε, i.e. |R 0 (t)-R mid (t) | is not less than 5 epsilon, and the step S65 is carried out; otherwise, step S66 is entered; wherein epsilon is the precision requirement of iterative solution, deltaL is the total capacity of the initial system added loader is C W The load quantity which can be borne by the wind turbine generator;
s65, solving target value R by chord cut method q (t) if |R q (t)-R 0 (t)|＞|R q-1 (t)-R 0 (t) |, indicating that the oscillation phenomenon is generated during the q-th iteration, and proceeding to step S66; otherwise, q=q+1, repeating step S65, and continuing to iteratively solve the target value R using the chord cut method q (t)；
S66, solving target value R by using dichotomy q (t), wherein R is q (t) obtaining a reliability index for the (t) th time and the (q) th time iteration, and entering step S67;
s67, if|R q (t)-R 0 (t) |ε or more, then q=q+1, repeat step S66, continue to iteratively solve the target value R using the dichotomy q (t); otherwise, step S68 is entered;
s68, taking a load side as an access point, and calculating the ratio of the load quantity capable of being born more to the installed capacity of wind power under the condition that the reliability level of the system is kept unchanged before and after the wind power generation set is accessed:
R(C g ,L)＝R(C g +C W ,L+ΔL)
C W (t)＝ΔL t
wherein: c (C) W (t) is the wind power short-term operation credible capacity of the t hour; ΔL t Adding installed total capacity to initial System of C W The load quantity of multiple loads which can be born at t hours after the wind turbine generator, C g R (C g L) is the reliability level of the initial system in the face of load L, R (C) g +C W L+Δl) is the reliability level when a new system after adding a wind turbine into the initial system faces the load l+Δl;
s7, short-term operation credible capacity C based on wind power W (t) solving the reliability SOCC of the short-term running capacity of wind power t 。
2. The method for evaluating reliability of short-term running capacity of wind power based on payload capacity according to claim 1, characterized by: in the step S3, the load data is represented by probability pulses of time-series load; let the study period be T hours, and the load level at T hours be L t T=1, 2, …, T, probability of occurrence of load level at T hours isThe load data in the study period is expressed as l= { L 1 ,L 2 ,···,L t ,···,L T }。
3. The method for evaluating reliability of short-term running capacity of wind power based on payload capacity according to claim 1, characterized by: in the step S5, M wind turbines are added into the initial system, and the ACD method is used for calculating the loss of load probability LOLP of the new system t,M+K The method comprises the following steps:
s51, the effective capacity distribution of the wind turbine j is expressed as:
wherein: j=1, 2, …, M, P W,j (t) is the output of the wind turbine j at the moment t,for the effective capacity distribution of the wind turbine j, P FOR,Wj The forced outage probability of the wind turbine j is set;
s52, output P of wind turbine j W,j For discrete random variables, a discrete random variable P is defined W,j The v-order moment of (2) is:
p Wj ＝1-P FOR,Wj
wherein: p is p Wj P is the normal operation probability of the wind turbine j W,j Output of wind turbine j, beta v V-order moment which is the effective capacity of the wind turbine generator, wherein v is a positive integer;
s53, converting each order moment into each order center moment, wherein the formula is as follows:
wherein: m is M Wv Is the v-order central moment of the effective capacity of the wind turbine,taking n as v and arranging and combining;
s54, obtaining the accumulation amount of each order through the center distance of each order, wherein the relation between the accumulation amount of the first 8 orders and the center moment of each order is as follows:
wherein: k (K) Wv V-order cumulant for the effective capacity of the wind turbine;
s55, the accumulated quantity of each step of equivalent effective capacity of M wind turbines and K thermal power turbines is expressed as:
wherein: KW (KW) v For the v-order cumulant of equivalent effective capacity of M wind turbines and K thermal power turbines, KS v The v-order cumulant for equivalent effective capacity of K thermal power generating units, K Wj,v The v-order cumulant of the wind turbine j;
s56, the distribution of equivalent effective capacity is represented by accumulation through edge worth series expansion, and the distribution function of the effective capacity after the loading of M wind turbines and K thermal power turbines is represented by F M+K (x) The representation is:
wherein: f (F) M+K (x) Probability that the power generation capacity provided after loading of M wind turbines and K thermal power turbines is smaller than x; n (x) is a standard normal density function; n (N) (γ) (x) Gamma derivative for N (x); g Wv The normalized cumulative quantity is of v order, and sigma is the standard deviation;
s57, load level L for t hour t Using F M+K (L t ) Indicating that the power generation capacity at t hour is less than L t To obtain the probability of load loss LOLP at t hour t,M+K And F is equal to M+K (L t ) Is related to LOLP t,M+K ＝F M+K (L t )。
4. The method for evaluating reliability of short-term running capacity of wind power based on payload capacity according to claim 1, characterized by: in the step S7, the short-term operation capacity reliability SOCC of the wind power is obtained t Solving according to the following formula:
wherein: SOCC (solid State control) t For the reliability of short-term running capacity of the wind power at the t-th hour, C W For the total capacity of the installation of the wind turbineQuantity, deltaL t Adding installed total capacity to initial System of C W The load quantity which can be borne more at the t hour after the wind turbine generator.
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